Syllabus
Previous experience with general coding is assumed - The course is designed for students that have at least some basic coding experience. It does not need to be very advanced, but they should be aware of concepts such as for loop, if and else, variable or function.
No knowledge of Python is required for entering the course
| Week | Date | L/S | Topic | Lecturer | Deadline || --- | --- | --- | --- | --- | --- || 1 | 19.2. | S | Seminar 0: Setup (Jupyter, VScode, Git, OS basics) | Martin + Alena | || 1 | 20.2. | L | Python basics | Martin | || 2 | 27.2. | L | Python basics II | Jan | || 3 | 4.3. | S | Seminar 1: Basics | Alena | HW 1 || 3 | 5.3. | L | Numpy | Jan | || 4 | 12.3. | L | Pandas I | Martin | || 5 | 18.3. | S | Seminar 2: Numpy & pandas | Alena | HW 2 || 5 | 19.3. | L | Pandas II + Matplotlib | Martin | || 6 | 26.3. | L | Data formats, APIs | Jan | || 7 | 2.4. | S | Seminar 3: Data formats & APIs | Alena | HW 3 || 7 | 8.4. | - | MIDTERM | Alena, Jan & Martin | || 8 | 9.4 | L | Algorithmic problem solving | Jan | || 9 | 15.4. | S | MIDTERM solution | Alena | || 9 | 16.4. | L | Data science | Martin | || 10 | 23.4. | L | How to code (avoiding spaghetti code) | Martin | Project proposal || 11 | 29.4. | S | Seminar 5: Data science case-study | Alena | || 11 | 30.4. | L | Databases | Jan | Topic approved || 12 | 7.5. | L | Guest Lecture + Beer after lecture @ https://pivo-klub.cz/ | TBD | || 13 | 12.-16.5. | - | WiP: Project consultations | Alena, Jan & Martin | || 14 | 20.-23.5. | - | WiP: Project consultations | Alena, Jan & Martin | |
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The course is taught in person and we expect students to come to the class to attend the lectures and seminars.
The aim of the course is to provide hands-on experience in programming in Python with a special emphasis on data manipulation and processing.
Students will get the basics of Pandas, Numpy or Matplotlib and also collect web data with API requests and BeatifiulSoup. The students will also be guided through modern social-coding and open-source technologies such as GitHub, Jupyter and Open Data.
The students will gain experience using the data from the IES website and subject evaluation protocols.
The course would make use of the DataCamp online sources ( https://www.datacamp.com ) to provide the students with reliable yet simple resources for learning Python programming.